Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations28831
Missing cells0
Missing cells (%)0.0%
Duplicate rows7
Duplicate rows (%)< 0.1%
Total size in memory21.2 MiB
Average record size in memory770.4 B

Variable types

Numeric10
Categorical10
Boolean1

Alerts

Dataset has 7 (< 0.1%) duplicate rowsDuplicates
cons.conf.idx is highly overall correlated with monthHigh correlation
cons.price.idx is highly overall correlated with contact and 2 other fieldsHigh correlation
contact is highly overall correlated with cons.price.idx and 2 other fieldsHigh correlation
emp.var.rate is highly overall correlated with cons.price.idx and 3 other fieldsHigh correlation
euribor3m is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
housing is highly overall correlated with loanHigh correlation
loan is highly overall correlated with housingHigh correlation
month is highly overall correlated with cons.conf.idx and 5 other fieldsHigh correlation
nr.employed is highly overall correlated with contact and 3 other fieldsHigh correlation
pdays is highly overall correlated with poutcome and 1 other fieldsHigh correlation
poutcome is highly overall correlated with pdays and 1 other fieldsHigh correlation
previous is highly overall correlated with pdays and 1 other fieldsHigh correlation
default is highly imbalanced (53.0%) Imbalance
loan is highly imbalanced (51.2%) Imbalance
poutcome is highly imbalanced (56.5%) Imbalance
previous has 24840 (86.2%) zeros Zeros

Reproduction

Analysis started2024-11-06 00:25:52.891696
Analysis finished2024-11-06 00:26:05.702110
Duration12.81 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct78
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.042177
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.4 KiB
2024-11-06T01:26:05.796747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.450542
Coefficient of variation (CV)0.26098836
Kurtosis0.83026769
Mean40.042177
Median Absolute Deviation (MAD)7
Skewness0.79187196
Sum1154456
Variance109.21383
MonotonicityNot monotonic
2024-11-06T01:26:05.957106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 1368
 
4.7%
32 1312
 
4.6%
35 1260
 
4.4%
33 1259
 
4.4%
36 1230
 
4.3%
30 1196
 
4.1%
34 1184
 
4.1%
37 1051
 
3.6%
39 1008
 
3.5%
29 999
 
3.5%
Other values (68) 16964
58.8%
ValueCountFrequency (%)
17 5
 
< 0.1%
18 17
 
0.1%
19 31
 
0.1%
20 41
 
0.1%
21 72
 
0.2%
22 98
 
0.3%
23 159
 
0.6%
24 321
1.1%
25 436
1.5%
26 498
1.7%
ValueCountFrequency (%)
98 2
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
92 4
 
< 0.1%
91 2
 
< 0.1%
89 2
 
< 0.1%
88 17
0.1%
87 1
 
< 0.1%
86 5
 
< 0.1%
85 8
< 0.1%

job
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
admin.
7238 
blue-collar
6538 
technician
4714 
services
2764 
management
2047 
Other values (7)
5530 

Length

Max length13
Median length12
Mean length8.9667719
Min length6

Characters and Unicode

Total characters258521
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowadmin.
2nd rowretired
3rd rowservices
4th rowservices
5th rowblue-collar

Common Values

ValueCountFrequency (%)
admin. 7238
25.1%
blue-collar 6538
22.7%
technician 4714
16.4%
services 2764
 
9.6%
management 2047
 
7.1%
retired 1219
 
4.2%
entrepreneur 1019
 
3.5%
self-employed 1015
 
3.5%
housemaid 726
 
2.5%
unemployed 702
 
2.4%
Other values (2) 849
 
2.9%

Length

2024-11-06T01:26:06.119739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 7238
25.1%
blue-collar 6538
22.7%
technician 4714
16.4%
services 2764
 
9.6%
management 2047
 
7.1%
retired 1219
 
4.2%
entrepreneur 1019
 
3.5%
self-employed 1015
 
3.5%
housemaid 726
 
2.5%
unemployed 702
 
2.4%
Other values (2) 849
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 33168
12.8%
n 24837
 
9.6%
a 23310
 
9.0%
l 22346
 
8.6%
i 21375
 
8.3%
c 18730
 
7.2%
r 14797
 
5.7%
m 13775
 
5.3%
d 11505
 
4.5%
t 10209
 
3.9%
Other values (14) 64469
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 258521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 33168
12.8%
n 24837
 
9.6%
a 23310
 
9.0%
l 22346
 
8.6%
i 21375
 
8.3%
c 18730
 
7.2%
r 14797
 
5.7%
m 13775
 
5.3%
d 11505
 
4.5%
t 10209
 
3.9%
Other values (14) 64469
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 258521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 33168
12.8%
n 24837
 
9.6%
a 23310
 
9.0%
l 22346
 
8.6%
i 21375
 
8.3%
c 18730
 
7.2%
r 14797
 
5.7%
m 13775
 
5.3%
d 11505
 
4.5%
t 10209
 
3.9%
Other values (14) 64469
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 258521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 33168
12.8%
n 24837
 
9.6%
a 23310
 
9.0%
l 22346
 
8.6%
i 21375
 
8.3%
c 18730
 
7.2%
r 14797
 
5.7%
m 13775
 
5.3%
d 11505
 
4.5%
t 10209
 
3.9%
Other values (14) 64469
24.9%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
married
17453 
single
8124 
divorced
3207 
unknown
 
47

Length

Max length8
Median length7
Mean length6.8294544
Min length6

Characters and Unicode

Total characters196900
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowsingle
5th rowmarried

Common Values

ValueCountFrequency (%)
married 17453
60.5%
single 8124
28.2%
divorced 3207
 
11.1%
unknown 47
 
0.2%

Length

2024-11-06T01:26:06.278288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T01:26:06.411426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
married 17453
60.5%
single 8124
28.2%
divorced 3207
 
11.1%
unknown 47
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 38113
19.4%
i 28784
14.6%
e 28784
14.6%
d 23867
12.1%
m 17453
8.9%
a 17453
8.9%
n 8265
 
4.2%
s 8124
 
4.1%
g 8124
 
4.1%
l 8124
 
4.1%
Other values (6) 9809
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 196900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 38113
19.4%
i 28784
14.6%
e 28784
14.6%
d 23867
12.1%
m 17453
8.9%
a 17453
8.9%
n 8265
 
4.2%
s 8124
 
4.1%
g 8124
 
4.1%
l 8124
 
4.1%
Other values (6) 9809
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 196900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 38113
19.4%
i 28784
14.6%
e 28784
14.6%
d 23867
12.1%
m 17453
8.9%
a 17453
8.9%
n 8265
 
4.2%
s 8124
 
4.1%
g 8124
 
4.1%
l 8124
 
4.1%
Other values (6) 9809
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 196900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 38113
19.4%
i 28784
14.6%
e 28784
14.6%
d 23867
12.1%
m 17453
8.9%
a 17453
8.9%
n 8265
 
4.2%
s 8124
 
4.1%
g 8124
 
4.1%
l 8124
 
4.1%
Other values (6) 9809
 
5.0%

education
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
university.degree
8579 
high.school
6615 
basic.9y
4195 
professional.course
3651 
basic.4y
2950 
Other values (3)
2841 

Length

Max length19
Median length17
Mean length12.718359
Min length7

Characters and Unicode

Total characters366683
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhigh.school
2nd rowbasic.4y
3rd rowhigh.school
4th rowprofessional.course
5th rowbasic.4y

Common Values

ValueCountFrequency (%)
university.degree 8579
29.8%
high.school 6615
22.9%
basic.9y 4195
14.6%
professional.course 3651
12.7%
basic.4y 2950
 
10.2%
basic.6y 1620
 
5.6%
unknown 1208
 
4.2%
illiterate 13
 
< 0.1%

Length

2024-11-06T01:26:06.538912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T01:26:06.664486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 8579
29.8%
high.school 6615
22.9%
basic.9y 4195
14.6%
professional.course 3651
12.7%
basic.4y 2950
 
10.2%
basic.6y 1620
 
5.6%
unknown 1208
 
4.2%
illiterate 13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 41644
 
11.4%
i 36215
 
9.9%
s 34912
 
9.5%
. 27610
 
7.5%
o 25391
 
6.9%
r 24473
 
6.7%
h 19845
 
5.4%
c 19031
 
5.2%
y 17344
 
4.7%
n 15854
 
4.3%
Other values (15) 104364
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 366683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 41644
 
11.4%
i 36215
 
9.9%
s 34912
 
9.5%
. 27610
 
7.5%
o 25391
 
6.9%
r 24473
 
6.7%
h 19845
 
5.4%
c 19031
 
5.2%
y 17344
 
4.7%
n 15854
 
4.3%
Other values (15) 104364
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 366683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 41644
 
11.4%
i 36215
 
9.9%
s 34912
 
9.5%
. 27610
 
7.5%
o 25391
 
6.9%
r 24473
 
6.7%
h 19845
 
5.4%
c 19031
 
5.2%
y 17344
 
4.7%
n 15854
 
4.3%
Other values (15) 104364
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 366683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 41644
 
11.4%
i 36215
 
9.9%
s 34912
 
9.5%
. 27610
 
7.5%
o 25391
 
6.9%
r 24473
 
6.7%
h 19845
 
5.4%
c 19031
 
5.2%
y 17344
 
4.7%
n 15854
 
4.3%
Other values (15) 104364
28.5%

default
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
no
22747 
unknown
6081 
yes
 
3

Length

Max length7
Median length2
Mean length3.0546981
Min length2

Characters and Unicode

Total characters88070
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowunknown

Common Values

ValueCountFrequency (%)
no 22747
78.9%
unknown 6081
 
21.1%
yes 3
 
< 0.1%

Length

2024-11-06T01:26:06.810601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T01:26:06.977271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 22747
78.9%
unknown 6081
 
21.1%
yes 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 40990
46.5%
o 28828
32.7%
u 6081
 
6.9%
k 6081
 
6.9%
w 6081
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 88070
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 40990
46.5%
o 28828
32.7%
u 6081
 
6.9%
k 6081
 
6.9%
w 6081
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 88070
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 40990
46.5%
o 28828
32.7%
u 6081
 
6.9%
k 6081
 
6.9%
w 6081
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 88070
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 40990
46.5%
o 28828
32.7%
u 6081
 
6.9%
k 6081
 
6.9%
w 6081
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

housing
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
yes
15130 
no
13012 
unknown
 
689

Length

Max length7
Median length3
Mean length2.6442718
Min length2

Characters and Unicode

Total characters76237
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowyes
3rd rowyes
4th rowyes
5th rowyes

Common Values

ValueCountFrequency (%)
yes 15130
52.5%
no 13012
45.1%
unknown 689
 
2.4%

Length

2024-11-06T01:26:07.095439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T01:26:07.222952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
yes 15130
52.5%
no 13012
45.1%
unknown 689
 
2.4%

Most occurring characters

ValueCountFrequency (%)
y 15130
19.8%
e 15130
19.8%
s 15130
19.8%
n 15079
19.8%
o 13701
18.0%
u 689
 
0.9%
k 689
 
0.9%
w 689
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76237
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
y 15130
19.8%
e 15130
19.8%
s 15130
19.8%
n 15079
19.8%
o 13701
18.0%
u 689
 
0.9%
k 689
 
0.9%
w 689
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76237
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
y 15130
19.8%
e 15130
19.8%
s 15130
19.8%
n 15079
19.8%
o 13701
18.0%
u 689
 
0.9%
k 689
 
0.9%
w 689
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76237
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
y 15130
19.8%
e 15130
19.8%
s 15130
19.8%
n 15079
19.8%
o 13701
18.0%
u 689
 
0.9%
k 689
 
0.9%
w 689
 
0.9%

loan
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
no
23734 
yes
4408 
unknown
 
689

Length

Max length7
Median length2
Mean length2.2723804
Min length2

Characters and Unicode

Total characters65515
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 23734
82.3%
yes 4408
 
15.3%
unknown 689
 
2.4%

Length

2024-11-06T01:26:07.339466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T01:26:07.444976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 23734
82.3%
yes 4408
 
15.3%
unknown 689
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 25801
39.4%
o 24423
37.3%
y 4408
 
6.7%
e 4408
 
6.7%
s 4408
 
6.7%
u 689
 
1.1%
k 689
 
1.1%
w 689
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65515
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 25801
39.4%
o 24423
37.3%
y 4408
 
6.7%
e 4408
 
6.7%
s 4408
 
6.7%
u 689
 
1.1%
k 689
 
1.1%
w 689
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65515
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 25801
39.4%
o 24423
37.3%
y 4408
 
6.7%
e 4408
 
6.7%
s 4408
 
6.7%
u 689
 
1.1%
k 689
 
1.1%
w 689
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65515
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 25801
39.4%
o 24423
37.3%
y 4408
 
6.7%
e 4408
 
6.7%
s 4408
 
6.7%
u 689
 
1.1%
k 689
 
1.1%
w 689
 
1.1%

contact
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
cellular
18312 
telephone
10519 

Length

Max length9
Median length8
Mean length8.3648503
Min length8

Characters and Unicode

Total characters241167
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcellular
2nd rowcellular
3rd rowcellular
4th rowcellular
5th rowcellular

Common Values

ValueCountFrequency (%)
cellular 18312
63.5%
telephone 10519
36.5%

Length

2024-11-06T01:26:07.565489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T01:26:07.670833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular 18312
63.5%
telephone 10519
36.5%

Most occurring characters

ValueCountFrequency (%)
l 65455
27.1%
e 49869
20.7%
c 18312
 
7.6%
u 18312
 
7.6%
a 18312
 
7.6%
r 18312
 
7.6%
t 10519
 
4.4%
p 10519
 
4.4%
h 10519
 
4.4%
o 10519
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 241167
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 65455
27.1%
e 49869
20.7%
c 18312
 
7.6%
u 18312
 
7.6%
a 18312
 
7.6%
r 18312
 
7.6%
t 10519
 
4.4%
p 10519
 
4.4%
h 10519
 
4.4%
o 10519
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 241167
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 65455
27.1%
e 49869
20.7%
c 18312
 
7.6%
u 18312
 
7.6%
a 18312
 
7.6%
r 18312
 
7.6%
t 10519
 
4.4%
p 10519
 
4.4%
h 10519
 
4.4%
o 10519
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 241167
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 65455
27.1%
e 49869
20.7%
c 18312
 
7.6%
u 18312
 
7.6%
a 18312
 
7.6%
r 18312
 
7.6%
t 10519
 
4.4%
p 10519
 
4.4%
h 10519
 
4.4%
o 10519
 
4.4%

month
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
may
9595 
jul
5096 
aug
4319 
jun
3720 
nov
2853 
Other values (5)
3248 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters86493
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rownov
3rd rowmay
4th rowjul
5th rowaug

Common Values

ValueCountFrequency (%)
may 9595
33.3%
jul 5096
17.7%
aug 4319
15.0%
jun 3720
 
12.9%
nov 2853
 
9.9%
apr 1828
 
6.3%
oct 515
 
1.8%
sep 396
 
1.4%
mar 381
 
1.3%
dec 128
 
0.4%

Length

2024-11-06T01:26:07.781906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T01:26:07.912058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
may 9595
33.3%
jul 5096
17.7%
aug 4319
15.0%
jun 3720
 
12.9%
nov 2853
 
9.9%
apr 1828
 
6.3%
oct 515
 
1.8%
sep 396
 
1.4%
mar 381
 
1.3%
dec 128
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 16123
18.6%
u 13135
15.2%
m 9976
11.5%
y 9595
11.1%
j 8816
10.2%
n 6573
7.6%
l 5096
 
5.9%
g 4319
 
5.0%
o 3368
 
3.9%
v 2853
 
3.3%
Other values (7) 6639
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86493
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 16123
18.6%
u 13135
15.2%
m 9976
11.5%
y 9595
11.1%
j 8816
10.2%
n 6573
7.6%
l 5096
 
5.9%
g 4319
 
5.0%
o 3368
 
3.9%
v 2853
 
3.3%
Other values (7) 6639
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86493
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 16123
18.6%
u 13135
15.2%
m 9976
11.5%
y 9595
11.1%
j 8816
10.2%
n 6573
7.6%
l 5096
 
5.9%
g 4319
 
5.0%
o 3368
 
3.9%
v 2853
 
3.3%
Other values (7) 6639
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86493
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 16123
18.6%
u 13135
15.2%
m 9976
11.5%
y 9595
11.1%
j 8816
10.2%
n 6573
7.6%
l 5096
 
5.9%
g 4319
 
5.0%
o 3368
 
3.9%
v 2853
 
3.3%
Other values (7) 6639
7.7%

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
thu
6052 
mon
5970 
tue
5719 
wed
5660 
fri
5430 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters86493
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtue
2nd rowwed
3rd rowfri
4th rowwed
5th rowthu

Common Values

ValueCountFrequency (%)
thu 6052
21.0%
mon 5970
20.7%
tue 5719
19.8%
wed 5660
19.6%
fri 5430
18.8%

Length

2024-11-06T01:26:08.065075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T01:26:08.178607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
thu 6052
21.0%
mon 5970
20.7%
tue 5719
19.8%
wed 5660
19.6%
fri 5430
18.8%

Most occurring characters

ValueCountFrequency (%)
t 11771
13.6%
u 11771
13.6%
e 11379
13.2%
h 6052
7.0%
m 5970
6.9%
o 5970
6.9%
n 5970
6.9%
w 5660
6.5%
d 5660
6.5%
f 5430
6.3%
Other values (2) 10860
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86493
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 11771
13.6%
u 11771
13.6%
e 11379
13.2%
h 6052
7.0%
m 5970
6.9%
o 5970
6.9%
n 5970
6.9%
w 5660
6.5%
d 5660
6.5%
f 5430
6.3%
Other values (2) 10860
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86493
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 11771
13.6%
u 11771
13.6%
e 11379
13.2%
h 6052
7.0%
m 5970
6.9%
o 5970
6.9%
n 5970
6.9%
w 5660
6.5%
d 5660
6.5%
f 5430
6.3%
Other values (2) 10860
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86493
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 11771
13.6%
u 11771
13.6%
e 11379
13.2%
h 6052
7.0%
m 5970
6.9%
o 5970
6.9%
n 5970
6.9%
w 5660
6.5%
d 5660
6.5%
f 5430
6.3%
Other values (2) 10860
12.6%

duration
Real number (ℝ)

Distinct1444
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.81305
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size225.4 KiB
2024-11-06T01:26:08.321711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median179
Q3318
95-th percentile756
Maximum4918
Range4918
Interquartile range (IQR)216

Descriptive statistics

Standard deviation263.01787
Coefficient of variation (CV)1.0162466
Kurtosis21.358263
Mean258.81305
Median Absolute Deviation (MAD)93
Skewness3.3565869
Sum7461839
Variance69178.401
MonotonicityNot monotonic
2024-11-06T01:26:08.477298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 125
 
0.4%
136 122
 
0.4%
104 121
 
0.4%
89 119
 
0.4%
87 119
 
0.4%
128 116
 
0.4%
114 116
 
0.4%
72 114
 
0.4%
90 113
 
0.4%
97 113
 
0.4%
Other values (1434) 27653
95.9%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 3
 
< 0.1%
4 9
 
< 0.1%
5 22
0.1%
6 25
0.1%
7 33
0.1%
8 51
0.2%
9 52
0.2%
ValueCountFrequency (%)
4918 1
< 0.1%
4199 1
< 0.1%
3643 1
< 0.1%
3631 1
< 0.1%
3509 1
< 0.1%
3366 1
< 0.1%
3322 1
< 0.1%
3284 1
< 0.1%
3183 1
< 0.1%
3094 1
< 0.1%

campaign
Real number (ℝ)

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5760813
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.4 KiB
2024-11-06T01:26:08.614119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8182355
Coefficient of variation (CV)1.094001
Kurtosis38.4558
Mean2.5760813
Median Absolute Deviation (MAD)1
Skewness4.8537343
Sum74271
Variance7.9424511
MonotonicityNot monotonic
2024-11-06T01:26:08.750630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 12445
43.2%
2 7314
25.4%
3 3702
 
12.8%
4 1869
 
6.5%
5 1096
 
3.8%
6 689
 
2.4%
7 442
 
1.5%
8 281
 
1.0%
9 200
 
0.7%
10 160
 
0.6%
Other values (31) 633
 
2.2%
ValueCountFrequency (%)
1 12445
43.2%
2 7314
25.4%
3 3702
 
12.8%
4 1869
 
6.5%
5 1096
 
3.8%
6 689
 
2.4%
7 442
 
1.5%
8 281
 
1.0%
9 200
 
0.7%
10 160
 
0.6%
ValueCountFrequency (%)
56 1
 
< 0.1%
43 2
< 0.1%
42 1
 
< 0.1%
40 2
< 0.1%
39 1
 
< 0.1%
37 1
 
< 0.1%
35 4
< 0.1%
34 3
< 0.1%
33 3
< 0.1%
32 4
< 0.1%

pdays
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.73376
Minimum0
Maximum999
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size225.4 KiB
2024-11-06T01:26:08.883783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186.273
Coefficient of variation (CV)0.1934834
Kurtosis22.423605
Mean962.73376
Median Absolute Deviation (MAD)0
Skewness-4.9418209
Sum27756577
Variance34697.63
MonotonicityNot monotonic
2024-11-06T01:26:09.016332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
999 27778
96.3%
3 309
 
1.1%
6 279
 
1.0%
4 82
 
0.3%
7 48
 
0.2%
12 43
 
0.1%
2 43
 
0.1%
10 38
 
0.1%
9 34
 
0.1%
5 28
 
0.1%
Other values (16) 149
 
0.5%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 20
 
0.1%
2 43
 
0.1%
3 309
1.1%
4 82
 
0.3%
5 28
 
0.1%
6 279
1.0%
7 48
 
0.2%
8 11
 
< 0.1%
9 34
 
0.1%
ValueCountFrequency (%)
999 27778
96.3%
27 1
 
< 0.1%
26 1
 
< 0.1%
22 3
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
< 0.1%
18 5
 
< 0.1%
17 7
 
< 0.1%
16 10
 
< 0.1%

previous
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17453436
Minimum0
Maximum6
Zeros24840
Zeros (%)86.2%
Negative0
Negative (%)0.0%
Memory size225.4 KiB
2024-11-06T01:26:09.127811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4955488
Coefficient of variation (CV)2.8392622
Kurtosis19.784113
Mean0.17453436
Median Absolute Deviation (MAD)0
Skewness3.8059583
Sum5032
Variance0.24556861
MonotonicityNot monotonic
2024-11-06T01:26:09.238322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 24840
86.2%
1 3258
 
11.3%
2 512
 
1.8%
3 157
 
0.5%
4 45
 
0.2%
5 15
 
0.1%
6 4
 
< 0.1%
ValueCountFrequency (%)
0 24840
86.2%
1 3258
 
11.3%
2 512
 
1.8%
3 157
 
0.5%
4 45
 
0.2%
5 15
 
0.1%
6 4
 
< 0.1%
ValueCountFrequency (%)
6 4
 
< 0.1%
5 15
 
0.1%
4 45
 
0.2%
3 157
 
0.5%
2 512
 
1.8%
1 3258
 
11.3%
0 24840
86.2%

poutcome
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
nonexistent
24840 
failure
3032 
success
 
959

Length

Max length11
Median length11
Mean length10.44629
Min length7

Characters and Unicode

Total characters301177
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rowsuccess
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent 24840
86.2%
failure 3032
 
10.5%
success 959
 
3.3%

Length

2024-11-06T01:26:09.375873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T01:26:09.549041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent 24840
86.2%
failure 3032
 
10.5%
success 959
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n 74520
24.7%
e 53671
17.8%
t 49680
16.5%
i 27872
 
9.3%
s 27717
 
9.2%
x 24840
 
8.2%
o 24840
 
8.2%
u 3991
 
1.3%
f 3032
 
1.0%
a 3032
 
1.0%
Other values (3) 7982
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 301177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 74520
24.7%
e 53671
17.8%
t 49680
16.5%
i 27872
 
9.3%
s 27717
 
9.2%
x 24840
 
8.2%
o 24840
 
8.2%
u 3991
 
1.3%
f 3032
 
1.0%
a 3032
 
1.0%
Other values (3) 7982
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 301177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 74520
24.7%
e 53671
17.8%
t 49680
16.5%
i 27872
 
9.3%
s 27717
 
9.2%
x 24840
 
8.2%
o 24840
 
8.2%
u 3991
 
1.3%
f 3032
 
1.0%
a 3032
 
1.0%
Other values (3) 7982
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 301177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 74520
24.7%
e 53671
17.8%
t 49680
16.5%
i 27872
 
9.3%
s 27717
 
9.2%
x 24840
 
8.2%
o 24840
 
8.2%
u 3991
 
1.3%
f 3032
 
1.0%
a 3032
 
1.0%
Other values (3) 7982
 
2.7%

emp.var.rate
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.084981444
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative11999
Negative (%)41.6%
Memory size225.4 KiB
2024-11-06T01:26:09.644650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5717849
Coefficient of variation (CV)18.495625
Kurtosis-1.0549905
Mean0.084981444
Median Absolute Deviation (MAD)0.3
Skewness-0.72942595
Sum2450.1
Variance2.4705077
MonotonicityNot monotonic
2024-11-06T01:26:09.757197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 11425
39.6%
-1.8 6397
22.2%
1.1 5407
18.8%
-0.1 2576
 
8.9%
-2.9 1171
 
4.1%
-3.4 754
 
2.6%
-1.7 539
 
1.9%
-1.1 434
 
1.5%
-3 122
 
0.4%
-0.2 6
 
< 0.1%
ValueCountFrequency (%)
-3.4 754
 
2.6%
-3 122
 
0.4%
-2.9 1171
 
4.1%
-1.8 6397
22.2%
-1.7 539
 
1.9%
-1.1 434
 
1.5%
-0.2 6
 
< 0.1%
-0.1 2576
 
8.9%
1.1 5407
18.8%
1.4 11425
39.6%
ValueCountFrequency (%)
1.4 11425
39.6%
1.1 5407
18.8%
-0.1 2576
 
8.9%
-0.2 6
 
< 0.1%
-1.1 434
 
1.5%
-1.7 539
 
1.9%
-1.8 6397
22.2%
-2.9 1171
 
4.1%
-3 122
 
0.4%
-3.4 754
 
2.6%

cons.price.idx
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.575503
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.4 KiB
2024-11-06T01:26:09.872775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.57848007
Coefficient of variation (CV)0.0061819606
Kurtosis-0.82469536
Mean93.575503
Median Absolute Deviation (MAD)0.306
Skewness-0.23805539
Sum2697875.3
Variance0.3346392
MonotonicityNot monotonic
2024-11-06T01:26:10.001918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 5407
18.8%
93.918 4762
16.5%
92.893 4039
14.0%
93.444 3614
12.5%
94.465 3049
10.6%
93.2 2527
8.8%
93.075 1711
 
5.9%
92.201 546
 
1.9%
92.963 505
 
1.8%
92.431 325
 
1.1%
Other values (16) 2346
8.1%
ValueCountFrequency (%)
92.201 546
 
1.9%
92.379 184
 
0.6%
92.431 325
 
1.1%
92.469 120
 
0.4%
92.649 245
 
0.8%
92.713 122
 
0.4%
92.756 6
 
< 0.1%
92.843 201
 
0.7%
92.893 4039
14.0%
92.963 505
 
1.8%
ValueCountFrequency (%)
94.767 81
 
0.3%
94.601 141
 
0.5%
94.465 3049
10.6%
94.215 214
 
0.7%
94.199 212
 
0.7%
94.055 166
 
0.6%
94.027 159
 
0.6%
93.994 5407
18.8%
93.918 4762
16.5%
93.876 149
 
0.5%

cons.conf.idx
Real number (ℝ)

High correlation 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.502879
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative28831
Negative (%)100.0%
Memory size225.4 KiB
2024-11-06T01:26:10.123488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.6264251
Coefficient of variation (CV)-0.1142246
Kurtosis-0.33719513
Mean-40.502879
Median Absolute Deviation (MAD)4.4
Skewness0.31567468
Sum-1167738.5
Variance21.403809
MonotonicityNot monotonic
2024-11-06T01:26:10.248970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 5407
18.8%
-42.7 4762
16.5%
-46.2 4039
14.0%
-36.1 3614
12.5%
-41.8 3049
10.6%
-42 2527
8.8%
-47.1 1711
 
5.9%
-31.4 546
 
1.9%
-40.8 505
 
1.8%
-26.9 325
 
1.1%
Other values (16) 2346
8.1%
ValueCountFrequency (%)
-50.8 81
 
0.3%
-50 201
 
0.7%
-49.5 141
 
0.5%
-47.1 1711
 
5.9%
-46.2 4039
14.0%
-45.9 6
 
< 0.1%
-42.7 4762
16.5%
-42 2527
8.8%
-41.8 3049
10.6%
-40.8 505
 
1.8%
ValueCountFrequency (%)
-26.9 325
 
1.1%
-29.8 184
 
0.6%
-30.1 245
 
0.8%
-31.4 546
 
1.9%
-33 122
 
0.4%
-33.6 120
 
0.4%
-34.6 117
 
0.4%
-34.8 180
 
0.6%
-36.1 3614
12.5%
-36.4 5407
18.8%

euribor3m
Real number (ℝ)

High correlation 

Distinct308
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6257957
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.4 KiB
2024-11-06T01:26:10.387119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.73324
Coefficient of variation (CV)0.47803025
Kurtosis-1.3989547
Mean3.6257957
Median Absolute Deviation (MAD)0.108
Skewness-0.71458356
Sum104535.32
Variance3.004121
MonotonicityNot monotonic
2024-11-06T01:26:10.538700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857 1972
 
6.8%
4.962 1813
 
6.3%
4.963 1758
 
6.1%
4.961 1368
 
4.7%
4.856 855
 
3.0%
4.964 805
 
2.8%
1.405 805
 
2.8%
4.965 749
 
2.6%
4.864 730
 
2.5%
4.96 728
 
2.5%
Other values (298) 17248
59.8%
ValueCountFrequency (%)
0.634 5
 
< 0.1%
0.635 30
0.1%
0.636 8
 
< 0.1%
0.637 4
 
< 0.1%
0.638 7
 
< 0.1%
0.639 11
 
< 0.1%
0.64 6
 
< 0.1%
0.642 25
0.1%
0.643 14
< 0.1%
0.644 26
0.1%
ValueCountFrequency (%)
5.045 4
 
< 0.1%
5 6
 
< 0.1%
4.97 124
 
0.4%
4.968 718
 
2.5%
4.967 453
 
1.6%
4.966 450
 
1.6%
4.965 749
2.6%
4.964 805
2.8%
4.963 1758
6.1%
4.962 1813
6.3%

nr.employed
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.2786
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size225.4 KiB
2024-11-06T01:26:10.659277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.148194
Coefficient of variation (CV)0.013962513
Kurtosis-0.00048912893
Mean5167.2786
Median Absolute Deviation (MAD)37.1
Skewness-1.0467243
Sum1.4897781 × 108
Variance5205.3619
MonotonicityNot monotonic
2024-11-06T01:26:10.771795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 11425
39.6%
5099.1 5951
20.6%
5191 5407
18.8%
5195.8 2576
 
8.9%
5076.2 1171
 
4.1%
5017.5 754
 
2.6%
4991.6 539
 
1.9%
5008.7 446
 
1.5%
4963.6 434
 
1.5%
5023.5 122
 
0.4%
ValueCountFrequency (%)
4963.6 434
 
1.5%
4991.6 539
 
1.9%
5008.7 446
 
1.5%
5017.5 754
 
2.6%
5023.5 122
 
0.4%
5076.2 1171
 
4.1%
5099.1 5951
20.6%
5176.3 6
 
< 0.1%
5191 5407
18.8%
5195.8 2576
8.9%
ValueCountFrequency (%)
5228.1 11425
39.6%
5195.8 2576
 
8.9%
5191 5407
18.8%
5176.3 6
 
< 0.1%
5099.1 5951
20.6%
5076.2 1171
 
4.1%
5023.5 122
 
0.4%
5017.5 754
 
2.6%
5008.7 446
 
1.5%
4991.6 539
 
1.9%

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
False
25613 
True
3218 
ValueCountFrequency (%)
False 25613
88.8%
True 3218
 
11.2%
2024-11-06T01:26:10.871341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Interactions

2024-11-06T01:26:04.057135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:54.779199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:55.960039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:56.967289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:58.036537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:59.049655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:00.070565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:01.106410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:02.093110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:03.081072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:04.166657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:54.943806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:56.071557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:57.079402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:58.148936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:59.170583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:00.178211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:01.216919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:02.203713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:03.191679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:04.329737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:55.088968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:56.172077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:57.179978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:58.252501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:59.272694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:00.275141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:01.318531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:02.302314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:03.289255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:04.428249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:55.214456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:56.278228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:57.279552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:58.354014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:59.374122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:00.369889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:01.418133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:02.400827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:03.387738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:04.525851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:55.321964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:56.377719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:57.378747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:58.454124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:59.473832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:00.465874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:01.517775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:02.497952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:03.485290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:04.626363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:55.439517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:56.481351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:57.484028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:58.559724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:59.573132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:00.569975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:01.618375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:02.601464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:03.585921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:04.718937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:55.542095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:56.577895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:57.580513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:58.655237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:59.669009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:00.661937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:01.714887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:02.696993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:03.679463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:04.814573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:55.646724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:56.674967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:57.742053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:58.754759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:59.771200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:00.756452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:01.809473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:02.795500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:03.776532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:04.910149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:55.753328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:56.774221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:57.840513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:58.851302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:59.868635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:00.856529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:01.904954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:02.889017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:03.870076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:05.002339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:55.856526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:56.869750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:57.936024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:58.948877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:25:59.968905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:01.012802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:01.997562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:02.985556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T01:26:03.962614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-06T01:26:10.968879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
agecampaigncons.conf.idxcons.price.idxcontactday_of_weekdefaultdurationeducationemp.var.rateeuribor3mhousingjobloanmaritalmonthnr.employedpdayspoutcomepreviousy
age1.0000.0070.1190.0430.1000.0250.145-0.0010.1160.0400.0500.0000.2490.0020.2620.0970.040-0.0090.111-0.0090.176
campaign0.0071.000-0.0060.0980.0640.0150.018-0.0810.0100.1590.1430.0240.0000.0250.0000.0490.1480.0570.047-0.0890.053
cons.conf.idx0.119-0.0061.0000.2450.4140.0450.138-0.0040.0650.2200.2330.0410.1100.0090.0680.6030.129-0.0780.371-0.1190.382
cons.price.idx0.0430.0980.2451.0000.6730.0480.1530.0070.0980.6640.4910.0650.1300.0110.0660.6770.4660.0610.388-0.2900.329
contact0.1000.0640.4140.6731.0000.0550.1330.0270.1160.4580.4650.0780.1250.0190.0630.6050.5000.1140.2400.2390.141
day_of_week0.0250.0150.0450.0480.0551.0000.0160.0050.0190.0350.1370.0150.0150.0000.0080.0660.0450.0050.0190.0080.018
default0.1450.0180.1380.1530.1330.0161.0000.0000.1710.1560.1590.0100.1580.0030.0930.1110.1400.0790.0750.0740.097
duration-0.001-0.081-0.0040.0070.0270.0050.0001.0000.000-0.066-0.0750.0010.0100.0090.0000.021-0.094-0.0800.0180.0430.376
education0.1160.0100.0650.0980.1160.0190.1710.0001.0000.0630.0640.0130.3600.0000.1130.0930.0670.0550.0400.0190.066
emp.var.rate0.0400.1590.2200.6640.4580.0350.156-0.0660.0631.0000.9400.0510.1330.0090.0620.6620.9450.2280.381-0.4410.336
euribor3m0.0500.1430.2330.4910.4650.1370.159-0.0750.0640.9401.0000.0520.1370.0090.0620.5970.9290.2770.421-0.4580.396
housing0.0000.0240.0410.0650.0780.0150.0100.0010.0130.0510.0521.0000.0120.7080.0130.0490.0410.0000.0150.0140.000
job0.2490.0000.1100.1300.1250.0150.1580.0100.3600.1330.1370.0121.0000.0120.1820.1080.1350.1360.0960.0560.152
loan0.0020.0250.0090.0110.0190.0000.0030.0090.0000.0090.0090.7080.0121.0000.0000.0170.0080.0000.0000.0000.000
marital0.2620.0000.0680.0660.0630.0080.0930.0000.1130.0620.0620.0130.1820.0001.0000.0500.0670.0420.0350.0260.053
month0.0970.0490.6030.6770.6050.0660.1110.0210.0930.6620.5970.0490.1080.0170.0501.0000.6040.2410.2460.1390.273
nr.employed0.0400.1480.1290.4660.5000.0450.140-0.0940.0670.9450.9290.0410.1350.0080.0670.6041.0000.2890.415-0.4430.407
pdays-0.0090.057-0.0780.0610.1140.0050.079-0.0800.0550.2280.2770.0000.1360.0000.0420.2410.2891.0000.954-0.5040.320
poutcome0.1110.0470.3710.3880.2400.0190.0750.0180.0400.3810.4210.0150.0960.0000.0350.2460.4150.9541.0000.7320.317
previous-0.009-0.089-0.119-0.2900.2390.0080.0740.0430.019-0.441-0.4580.0140.0560.0000.0260.139-0.443-0.5040.7321.0000.236
y0.1760.0530.3820.3290.1410.0180.0970.3760.0660.3360.3960.0000.1520.0000.0530.2730.4070.3200.3170.2361.000

Missing values

2024-11-06T01:26:05.167921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-06T01:26:05.534098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
035admin.marriedhigh.schoolnononocellularmaytue17229990nonexistent-1.892.893-46.21.2665099.1no
157retiredmarriedbasic.4ynoyesnocellularnovwed295132success-3.492.649-30.10.7165017.5no
250servicesmarriedhigh.schoolnoyesyescellularmayfri4419990nonexistent-1.892.893-46.21.2505099.1no
324servicessingleprofessional.coursenoyesnocellularjulwed13919990nonexistent1.493.918-42.74.9625228.1no
444blue-collarmarriedbasic.4yunknownyesnocellularaugthu28339990nonexistent1.493.444-36.14.9635228.1no
529admin.marrieduniversity.degreenononocellularaugthu11159990nonexistent-2.992.201-31.40.8735076.2no
625blue-collarsinglebasic.9ynonoyescellularjulthu15129990nonexistent1.493.918-42.74.9625228.1no
754admin.singleuniversity.degreenoyesnocellularaugwed10499990nonexistent1.493.444-36.14.9655228.1no
835admin.singleuniversity.degreenononocellularmaywed8829990nonexistent-1.892.893-46.21.3345099.1no
938blue-collarmarriedbasic.4yunknownyesnocellularjultue7129990nonexistent1.493.918-42.74.9615228.1no
agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
2882135managementmarrieduniversity.degreenononotelephonemaymon34929990nonexistent1.193.994-36.44.8575191.0no
2882246admin.marrieduniversity.degreenononocellularaugwed27319990nonexistent1.493.444-36.14.9675228.1no
2882334technicianmarriedprofessional.coursenonoyestelephonemayfri38829990nonexistent1.193.994-36.44.8555191.0no
2882432unemployedmarriedhigh.schoolunknownyesnocellularjulmon3839990nonexistent1.493.918-42.74.9625228.1no
2882526studentsingleprofessional.coursenoyesnocellularaugtue17629991failure-2.992.201-31.40.8835076.2yes
2882631admin.marriedunknownnoyesnocellularmaywed7119991failure-1.892.893-46.21.3345099.1no
2882737unemployedmarriedprofessional.coursenononocellularnovwed14419990nonexistent-0.193.200-42.04.1205195.8no
2882848blue-collarsinglebasic.4ynoyesnocellularmaytue120319991failure-1.892.893-46.21.2915099.1yes
2882925techniciansinglebasic.9ynoyesnocellularmayfri1449990nonexistent-1.892.893-46.21.2505099.1no
2883027blue-collarmarriedbasic.4yunknownnonotelephonejunwed7239990nonexistent1.494.465-41.84.9625228.1no

Duplicate rows

Most frequently occurring

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy# duplicates
024servicessinglehigh.schoolnoyesnocellularaprtue11419990nonexistent-1.893.075-47.11.4235099.1no2
132techniciansingleprofessional.coursenoyesnocellularjulthu12819990nonexistent1.493.918-42.74.9685228.1no2
235admin.marrieduniversity.degreenoyesnocellularmayfri34849990nonexistent-1.892.893-46.21.3135099.1no2
339admin.marrieduniversity.degreenononocellularnovtue12329990nonexistent-0.193.200-42.04.1535195.8no2
439blue-collarmarriedbasic.6ynononotelephonemaythu12419990nonexistent1.193.994-36.44.8555191.0no2
555servicesmarriedhigh.schoolunknownnonocellularaugmon3319990nonexistent1.493.444-36.14.9655228.1no2
671retiredsingleuniversity.degreenononotelephoneocttue12019990nonexistent-3.492.431-26.90.7425017.5no2